{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"id":"x0LKogzSjJeN"},"outputs":[],"source":["# install some necessary packages\n","!pip install wandb -qU\n","!pip install bitsandbytes -qU\n","!pip install peft -qU\n","!pip install datasets -qU\n","!pip install transformers -qU\n","!pip install loguru -qU\n","!pip install tokenizers -qU\n","!pip install sentencepiece -qU\n"]},{"cell_type":"code","execution_count":2,"metadata":{"id":"D8AhhjloSE4k","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1702348547197,"user_tz":300,"elapsed":15540,"user":{"displayName":"Gezheng Bai","userId":"07004837890649195177"}},"outputId":"83fe3c5d-f709-4270-ce4d-ff36da6b78a4"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}],"source":["# connect to google drive\n","from google.colab import drive\n","drive.mount('/content/drive')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Z760YEC5jFul","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1701553142537,"user_tz":300,"elapsed":10421,"user":{"displayName":"Gezheng Bai","userId":"07004837890649195177"}},"outputId":"3960ce88-4a35-4af7-fd7e-e630afee5919"},"outputs":[{"output_type":"stream","name":"stdout","text":["\n","    _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|\n","    _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n","    _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|\n","    _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\n","    _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|\n","\n","    To login, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n","Token: \n","Add token as git credential? (Y/n) n\n","Token is valid (permission: read).\n","Your token has been saved to /root/.cache/huggingface/token\n","Login successful\n"]}],"source":["!huggingface-cli login"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"bvgYD54kaxz9","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1701553112019,"user_tz":300,"elapsed":2606,"user":{"displayName":"Gezheng Bai","userId":"07004837890649195177"}},"outputId":"371d8b1e-fa60-4510-8482-4ffd757938bb"},"outputs":[{"output_type":"stream","name":"stderr","text":["\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mbaigzcu123\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"]},{"output_type":"execute_result","data":{"text/plain":["True"]},"metadata":{},"execution_count":3}],"source":["# wandb login\n","import os\n","import wandb\n","\n","# Set the WANDB_API_KEY environment variable\n","os.environ['WANDB_API_KEY'] = '87246aa35c0741ef13381fe3bead47e6d1eeebfb'  # Replace with your actual key\n","\n","# Login to wandb\n","wandb.login()\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"G5SD7h8OJQne"},"outputs":[],"source":["# import os and sys\n","\n","OUTPUT_DIR = '/content/drive/MyDrive/Hallucination/Llama2_7b_Finance_FT_3'\n","if not os.path.exists(OUTPUT_DIR):\n","    os.makedirs(OUTPUT_DIR)\n","\n","import sys"]},{"cell_type":"code","execution_count":3,"metadata":{"id":"3rd6Z9gRiGRh","executionInfo":{"status":"ok","timestamp":1702348561856,"user_tz":300,"elapsed":10715,"user":{"displayName":"Gezheng Bai","userId":"07004837890649195177"}}},"outputs":[],"source":["from typing import List, Dict, Optional\n","from loguru import logger\n","\n","import torch\n","import torch.nn as nn\n","import bitsandbytes as bnb\n","from datasets import load_dataset\n","import transformers\n","from transformers import (\n","    AutoModelForCausalLM,\n","    AutoTokenizer,\n","    TrainingArguments,\n","    Trainer,\n","    BitsAndBytesConfig\n",")\n","from peft import (\n","    TaskType,\n","    LoraConfig,\n","    get_peft_model,\n","    prepare_model_for_kbit_training,\n","    prepare_model_for_int8_training,\n","    set_peft_model_state_dict,\n","    get_peft_model_state_dict,\n",")\n","\n","from peft.utils import TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING"]},{"cell_type":"code","source":["mmlu_dataset_management = load_dataset(\"cais/mmlu\", name = 'management')"],"metadata":{"id":"25TAyUMIwWlo","executionInfo":{"status":"ok","timestamp":1702349848491,"user_tz":300,"elapsed":1583,"user":{"displayName":"Gezheng Bai","userId":"07004837890649195177"}}},"execution_count":32,"outputs":[]},{"cell_type":"code","source":["mmlu_dataset_management['test']"],"metadata":{"id":"lHVKDzUVwWoB","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1702349861751,"user_tz":300,"elapsed":565,"user":{"displayName":"Gezheng Bai","userId":"07004837890649195177"}},"outputId":"785c865a-0c26-441a-9662-8521d919a0f2"},"execution_count":33,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Dataset({\n","    features: ['question', 'subject', 'choices', 'answer'],\n","    num_rows: 103\n","})"]},"metadata":{},"execution_count":33}]},{"cell_type":"code","source":["mmlu_dataset_marketing = load_dataset(\"cais/mmlu\", name = 'marketing')"],"metadata":{"id":"eLd0QeLIwWrA","colab":{"base_uri":"https://localhost:8080/","height":145,"referenced_widgets":["2e5157b62da246afa6ae4c877dcf3ac2","035aa2e1d3924c219dae47f4d3adfa23","10e34e9710204a8db6811ea45f050b20","e9a4397d4bff489782c7ab47e785dd84","f48421e9d7ae46d7b7b7a729dc29b53c","bde84528061d46a29d3d098c90c6a4d3","9cbb65a11d00419ab61ce0b064989fcf","a8f53db2f6314cddaa9aca78c3ca5405","b56aee53b596489895c5587357e03de0","b43860b41e3c4ea6b69ff95f78ef01bd","5d62e897010a454b85d46a46a27fddde","1d04f0dda1e74c8e985d692c394b24aa","e69ddd2858204b00a2aabeafbff7f2c2","61933751780a420f90aa4340aebd0c01","fff325d2d3114e189fb6d9bf712a5101","5c19273e79ad4e8cb50857d224aa1688","d307b000b1424221a8dafa78651c0722","844d0ff2a222404c9fe0f09de2fdc0fc","2d577ea0dea64a038409327e940133e1","526e17cd7d7f4f119f6af7c6c36aadeb","5cd1483ecc9e42fdb32089bc941c869d","0ebba2653c2d41eaa4512f45b870ebf4","1b21f2cb994d48efafdd8aa1dd96776a","634ba69a50c54b60b27a93af859387f1","e691d613976848fda69a32e78ead56b9","f99d34bb80fe41b0ace905802141c33a","5ec7b3459d8f4547bf82798734db2d22","7e60f84a0ca54d3b8e2494dfc6478884","4c63fd5a1ac5430fa9dc639a440cf671","eaf7ecaff1f54266995f99d109e6268a","39d1181c3ba94e0ea6e581d7576af07c","33dde27edf07424896264880adea6f7f","1a55351a41044101b68a44d1083ac313","05bc036a83bd4b05a39b32e5adfbd1c9","3fb46c1424324279adc2f6b7ffb5ca80","c4893485a8cd4002940666ce92d0c9e6","a8600dea24184e4e82114845e660cc7f","a2e97266825e47b4aca7fa395633c5d8","1dfa0b69143b4d5aafaa9f76df609554","2efee65946fe4e52a2d66fa566334c67","8902d9ad48d64107849d49a5686e420c","73d98868224244189873bb9dbfac080b","197033f4da5c490d8a0d346a54524969","55f78abd29a54ea982f9f2cbbb01ff15"]},"executionInfo":{"status":"ok","timestamp":1702349894746,"user_tz":300,"elapsed":10652,"user":{"displayName":"Gezheng Bai","userId":"07004837890649195177"}},"outputId":"6e16afda-6c46-4da5-ca26-b5118d55e859"},"execution_count":34,"outputs":[{"output_type":"display_data","data":{"text/plain":["Generating auxiliary_train split:   0%|          | 0/99842 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"2e5157b62da246afa6ae4c877dcf3ac2"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating test split:   0%|          | 0/234 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"1d04f0dda1e74c8e985d692c394b24aa"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating validation split:   0%|          | 0/25 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"1b21f2cb994d48efafdd8aa1dd96776a"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating dev split:   0%|          | 0/5 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"05bc036a83bd4b05a39b32e5adfbd1c9"}},"metadata":{}}]},{"cell_type":"code","source":["type(mmlu_dataset_marketing)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"x0lDEtii9dQC","executionInfo":{"status":"ok","timestamp":1702349915898,"user_tz":300,"elapsed":275,"user":{"displayName":"Gezheng Bai","userId":"07004837890649195177"}},"outputId":"3411a829-e0d1-4833-9c17-0bd125188542"},"execution_count":35,"outputs":[{"output_type":"execute_result","data":{"text/plain":["datasets.dataset_dict.DatasetDict"]},"metadata":{},"execution_count":35}]},{"cell_type":"markdown","metadata":{"id":"XElKkj3JZDpd"},"source":["# Set up training hyperparameters"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ugj3RXjZlxBQ"},"outputs":[],"source":["# prepare steps\n","\n","# Set random seed for reproducibility\n","RANDOM_SEED = 3407\n","transformers.set_seed(RANDOM_SEED)\n","\n","# Set up the training hyperparameter\n","MICRO_BATCH_SIZE = 64\n","BATCH_SIZE = 256\n","GRADIENT_ACCUMULATION_STEPS = BATCH_SIZE // MICRO_BATCH_SIZE\n","EPOCHS = 1  # One epoch takes ~6 hours, and 2 epochs may exceed 12-hour.\n","LEARNING_RATE = 2e-5  # Following stanford_alpaca\n","CUTOFF_LEN = 256  # 256 accounts for about 96% of the data. Shorter input, faster training/less VRAM\n","\n","LORA_R = 8  # Some LoRA parameters\n","LORA_ALPHA = 16\n","LORA_DROPOUT = 0.05\n","VAL_SET_SIZE = 2000\n","TARGET_MODULES = [\n","    'q_proj',\n","    'v_prol',\n","]\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"H5dbpwwFhKha"},"outputs":[],"source":["# DDP setting\n","device_map = 'auto'\n","world_size = int(os.environ.get('WORLD_SIZE', 1))\n","ddp = (world_size != 1)  # If more than one GPU, then DDP\n","if ddp:\n","    device_map = {'': int(os.environ.get('LOCAL_RANK') or 0)}\n","    GRADIENT_ACCUMULATION_STEPS = GRADIENT_ACCUMULATION_STEPS // world_size"]},{"cell_type":"markdown","metadata":{"id":"clcO8Pxg6IwP"},"source":["# Helper functions"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Kow7yfXRmTax"},"outputs":[],"source":["def print_trainable_parameters(model):\n","    \"\"\"\n","    Prints the number of trainable parameters in the model.\n","    \"\"\"\n","    trainable_params = 0\n","    all_param = 0\n","    for _, param in model.named_parameters():\n","        all_param += param.numel()\n","        if param.requires_grad:\n","            trainable_params += param.numel()\n","    print(\n","        f\"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}\"\n","    )"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"jBUkwTSE3Zih"},"outputs":[],"source":["def generate_prompt(data_point):\n","    \"\"\"Gen. input text based on a prompt, task instruction, (context info.), and answer\n","\n","    :param data_point: dict: Data point\n","    :return: str: Input text\n","    \"\"\"\n","    # Samples with additional context into.\n","    if data_point['input']:\n","        text = 'Below is an instruction that describes a task, paired with an input that provides' \\\n","               ' further context. Write a response that appropriately completes the request.\\n\\n'\n","        text += f'### Instruction:\\n{data_point[\"instruction\"]}\\n\\n'\n","        text += f'### Input:\\n{data_point[\"input\"]}\\n\\n'\n","        text += f'### Response:\\n{data_point[\"output\"]}'\n","        return text\n","\n","    # Without\n","    else:\n","        text = 'Below is an instruction that describes a task. Write a response that ' \\\n","               'appropriately completes the request.\\n\\n'\n","        text += f'### Instruction:\\n{data_point[\"instruction\"]}\\n\\n'\n","        text += f'### Response:\\n{data_point[\"output\"]}'\n","        return text"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"_xSwms3P3bRf"},"outputs":[],"source":["def tokenize(prompt):\n","    \"\"\"Tokenize the input\n","\n","    :param prompt: str: Input text\n","    :return: dict: {'tokenised input text': list, 'mask': list}\n","    \"\"\"\n","    result = tokenizer(prompt, truncation=True, max_length=CUTOFF_LEN + 1, padding='max_length')\n","    return {\n","        'input_ids': result['input_ids'][:-1],\n","        'attention_mask': result['attention_mask'][:-1],\n","    }\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"v7_r_fTA4dz6"},"outputs":[],"source":["def generate_and_tokenize_prompt(data_point):\n","    \"\"\"This function masks out the labels for the input, so that our loss is computed only on the\n","    response.\"\"\"\n","    if data_point['input']:\n","        user_prompt = 'Below is an instruction that describes a task, paired with an input that ' \\\n","                      'provides further context. Write a response that appropriately completes ' \\\n","                      'the request.\\n\\n'\n","        user_prompt += f'### Instruction:\\n{data_point[\"instruction\"]}\\n\\n'\n","        user_prompt += f'### Input:\\n{data_point[\"input\"]}\\n\\n'\n","        user_prompt += f'### Response:\\n'\n","    else:\n","        user_prompt = 'Below is an instruction that describes a task. Write a response that ' \\\n","                      'appropriately completes the request.'\n","        user_prompt += f'### Instruction:\\n{data_point[\"instruction\"]}\\n\\n'\n","        user_prompt += f'### Response:\\n'\n","\n","    # Count the length of prompt tokens\n","    len_user_prompt_tokens = len(tokenizer(user_prompt,\n","                                           truncation=True,\n","                                           max_length=CUTOFF_LEN + 1,\n","                                           padding='max_length')['input_ids'])\n","    len_user_prompt_tokens -= 1  # Minus 1 (one) for eos token\n","\n","    # Tokenise the input, both prompt and output\n","    full_tokens = tokenizer(\n","        user_prompt + data_point['output'],\n","        truncation=True,\n","        max_length=CUTOFF_LEN + 1,\n","        padding='max_length',\n","    )['input_ids'][:-1]\n","    return {\n","        'input_ids': full_tokens,\n","        'labels': [-100] * len_user_prompt_tokens + full_tokens[len_user_prompt_tokens:],\n","        'attention_mask': [1] * (len(full_tokens)),\n","    }\n"]},{"cell_type":"markdown","metadata":{"id":"UW_rEmd_iJ6Y"},"source":["# Load Model&Tokenizer"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"3-0v7ggZiI1f","colab":{"base_uri":"https://localhost:8080/","height":496,"referenced_widgets":["c7242373f5b8429fa98e884ba09ecb77","07ccb0ff348640728efeeab0b3167cae","d987968930a4460884f14b17bfefcd62","63328f5a740c49b98b42f87162507778","7bfdc432506c444ea47dc27afc09ba08","5d8d8d4574434e66bddb237939a4ed8d","060771fa84174b458914b7571dfd63e6","870ef1e342374b9099886abd34a0311e","2067a45e0af847c5824f046e8d843b31","5745b04b5d3b4cdda22d005bf810bf6b","7a827b8e0dc345daa302a22dc1dcecb4","0a9c9e47b8c5449dacdbed522a69f612","579af906ec514d90a22b3d1b60a76b75","33993838c5354e76be634e05dda702a2","2d93536643c94b1785d149d305475219","067decd508884f1cb9e39f66562c8ba9","f28b8eaaabcd4adda9b0f98b44b97506","9c8c6041808346f483a1eee535897cb1","ff8507360a724777866dfa2d063fbb04","275be621a6a8445c92d8919695db3ec7","7541937c84494a5dbaaea5e7df617862","6d50b3fd902444cd92cd2c98482de240","3c33e5134b0b46f3921ff26fb4a9037e","34bda34b46d64d4ebd45a97a585a2407","9e2abe9e810c47c9a9f69629b6c667b1","66b6e6b574f14e73bb9dc44c4dde1b3e","06e2fd1f057044e99345a09ebf32fff7","424419251e734a39924ffe68dc196b89","de0f5df0123d4ff084ecb28d85af19e8","5b1a89fe685a43f4a9927508637ee282","999505dce6b94e86ba49fb787dd53fab","5e3c9922d6014d9cba4b42ef53306344","15e11b6aaf6f4159bfbd1e2efeeef8b5","192e8af9952b43eeb7d8663bbd21f19c","41e2b0c0032e42dbbd7c73a780243c69","f440577a1e3745af953c543ea68fa032","c3da141c290f4c7ca4bff6f464fd301a","88dad0dc09514f45b68e36c52b5d9953","dc15b35560cd40eea46f3f83f3f27a27","e864d3346f754730aebc1ba1222a8008","9e8ae3a963a242468c03ce5ca5f16edd","41206281e16944559f212ca9b8207598","49418e428b5e4102b1b9d23b8cf33147","5c15c74232ad4a03880c80f23fb5d291","805fdf7cced0438e9aefc0be516994dd","7ca4d8c6cf3045e9be1604d7386b86c6","d28cd5b50f9e4425827b9609283fd9b7","b0f436f3c99e45168ae7e108cb0b16cf","d7601fde1fc54fbd9a066eeb672ec9e1","7d51b64921d24896bd65667a1f19cb9d","1b652a511b1143398868046e56f6e4ee","ef415050ae3b4581a7846b852f64278c","1d2546cd8b3e4d1fa6e3ed556c44bc93","8adcbc1b890d46ebad75ab769174ebac","980415343d14458380b34562ba95b8b1","ecfa72fbe8e942338a96ce86f104bb0c","7a4d5c0e22504bc9a281f058fc90951a","a7671781ffdb41edbfb4f1d16b3f2ede","529c46f7160d469397079645f94df6ae","4013d58210174408bdb3170ce38961f6","1b01b18062ad406fb66d25dc1b804d88","cd980eb1304243a387f0d6cc1722dc9b","6f229fdd8b8447d38edf42af66fc90bc","fa9491191db640e0a2ca12b65840371e","6485317d8cad40058544f180728aa842","8fe4ae90ae1a4e7dac27b60a892378d5","a689c7bab0cb49a88b83d67c1af485a8","f90cc0c886384c0f9fa7047e7376bf93","6d5d81c4fbdf45219e645c18a2976fda","a9aa756ae5e04c41bd4ad3a0cb143149","2e42775ed00643b18377e097836d83fe","f681bf5361e3461690b39076e024ab89","2e5226e134c446fa8c1a4462e082fb8b","229a61e6112f4412ad24004e28847e0b","ef042a558c6b46cf961d4f85cb3127d8","ef002fb3182a448f8f5a1b8ef67b310c","553c227d265f46e0b9d675b62fbb0163","3022cf90879c4cf8ae104391bfb0bad6","a7a7573d04754cfba296753f5ca7bcdf","97cb9662243f442b9f03eae2e80dd6e2","9c0bf0ce94404685b499e2ca5c5b3931","52b4e72aa020484bab6d4603ae661150","df776ee95a9f4f96b743b71f6a648bf2","64f6b613d4aa4e598b95184d77dd3470","fb35d0376988433b9d2ff071cf3d941c","ff9706631c6848339ba263514fc904cd","ad84d27feda44691862f8371f5eff49e","ffb7b439697a46da9027caf5c93c356e","122449e23f7a403982a2f8a74fe04008","ede63d3f07ef49b68492f6212be4e80c","db6fc967f7484b2ab9582c0d9d89a5ec","328f89e4878344b68e76cb76cba1eadc","8290907d1e844d98b4db94b0b2178cea","c324786dff8e42c88199151354ef8537","251bcb7039c14885878ab632fe27f194","8a336de4e8fe43dd8040504b5db222cb","853fb7cb0aa047239d8c890ff218a086","cbc49edde003465581ecd9020f6d41b3","25477462c25b4d179f8db43172522b2d","fed0454c7cc64693a27729adb662604a","f12b961796184467b98a4a6ec5fab056","d1ef11d745e24230be5b7ae0f999b381","22d2330add5b414dac21820c4314b1c3","b320b4c7861e41fbb1d16d1b4f8b7bcc","820ee131a1654a7ca23c36473235913b","2b169f7d86c94c369025a5c3ebff3fdf","1fa3d847c0bb4a84bedf75f95894f340","427d86d0eea443409f1c0fea83cfdee1","2a4aec5b401b4bcaa3978374c3aca719","2451dd82a658432d97601e6bedf1f64d","7f187f5b52094c4e8cdd73e76fbae990","8e6cb6a28bdf43fd902a85a19a6300d9","488ea6bda9e24ab4b08aa5cbf9b7940e","eef054c036364b329377fb5daeec93fd","98cd9fec89af488e9b851321d72c7c3c","7032311867c24df8aab6f05c85475ef9","2829ed1fbd68417bb0a8e33f28c95913","bb3f80405af64c2192e2c80ea465dfc1","5cfec29668134c6ca675a92a523e554e","edd259ed149349e1b1e1c0cb018fd298","a07acd81c6514b7b98f0a5bf7a3986aa"]},"executionInfo":{"status":"ok","timestamp":1701553216185,"user_tz":300,"elapsed":54036,"user":{"displayName":"Gezheng Bai","userId":"07004837890649195177"}},"outputId":"ed849d89-ab60-432a-fdcc-3639622d14e4"},"outputs":[{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json:   0%|          | 0.00/746 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c7242373f5b8429fa98e884ba09ecb77"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer.model:   0%|          | 0.00/500k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"0a9c9e47b8c5449dacdbed522a69f612"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer.json:   0%|          | 0.00/1.84M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"3c33e5134b0b46f3921ff26fb4a9037e"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["special_tokens_map.json:   0%|          | 0.00/435 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"192e8af9952b43eeb7d8663bbd21f19c"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["config.json:   0%|          | 0.00/583 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"805fdf7cced0438e9aefc0be516994dd"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model.safetensors.index.json:   0%|          | 0.00/26.8k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ecfa72fbe8e942338a96ce86f104bb0c"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading shards:   0%|          | 0/2 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a689c7bab0cb49a88b83d67c1af485a8"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model-00001-of-00002.safetensors:   0%|          | 0.00/9.98G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"3022cf90879c4cf8ae104391bfb0bad6"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model-00002-of-00002.safetensors:   0%|          | 0.00/3.50G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"122449e23f7a403982a2f8a74fe04008"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"fed0454c7cc64693a27729adb662604a"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["generation_config.json:   0%|          | 0.00/179 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7f187f5b52094c4e8cdd73e76fbae990"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/transformers/generation/configuration_utils.py:381: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.9` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`. This was detected when initializing the generation config instance, which means the corresponding file may hold incorrect parameterization and should be fixed.\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/generation/configuration_utils.py:386: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.6` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`. This was detected when initializing the generation config instance, which means the corresponding file may hold incorrect parameterization and should be fixed.\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/peft/utils/other.py:136: FutureWarning: prepare_model_for_int8_training is deprecated and will be removed in a future version. Use prepare_model_for_kbit_training instead.\n","  warnings.warn(\n"]}],"source":["#from transformers import LlamaForCausalLM, LlamaTokenizer\n","\n","model_name = \"NousResearch/Llama-2-7b-hf\"\n","\n","# Load tokenizer\n","tokenizer = AutoTokenizer.from_pretrained(\n","    model_name,\n","    trust_remote_code=True,\n","    add_eos_token=True\n",")\n","#tokenizer.pad_token_id = 0 # unk. we want this to be different from the eos token\n","\n","# Read Llama2-7b\n","model = AutoModelForCausalLM.from_pretrained(\n","    model_name,\n","    load_in_8bit=True,\n","    trust_remote_code=True,\n","    device_map= 'auto',\n",")\n","\n","model = prepare_model_for_int8_training(model, use_gradient_checkpointing=True)"]},{"cell_type":"markdown","metadata":{"id":"UFzWrAeHnpeM"},"source":["# LoRA fine-tune"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"GRZAIkF7nlRh","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1701553220095,"user_tz":300,"elapsed":139,"user":{"displayName":"Gezheng Bai","userId":"07004837890649195177"}},"outputId":"94dbda7b-79d4-425a-a652-60044c97c188"},"outputs":[{"output_type":"stream","name":"stdout","text":["trainable params: 2097152 || all params: 6740512768 || trainable%: 0.03111264783824826\n"]}],"source":["# LoRA fine-tuning\n","\n","# Set up LoRA config\n","lora_config = LoraConfig(\n","    task_type= 'CAUSAL_LM',\n","    r=LORA_R, # LORA_R\n","    lora_alpha= LORA_ALPHA, # LORA_ALPHA\n","    lora_dropout= LORA_DROPOUT, # LORA_DROPOUT\n","    target_modules= TARGET_MODULES, # TARGET_MODULES\n","    bias='none',\n",")\n","model = get_peft_model(model, lora_config)\n","print_trainable_parameters(model)\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"pJWnX9FuyWHK"},"outputs":[],"source":["# this snippet helps us retry and continue training on some checkpoints\n","resume_from_checkpoint = None\n","if resume_from_checkpoint is not None:\n","    checkpoint_name = os.path.join(resume_from_checkpoint, 'pytorch_model.bin')\n","    if not os.path.exists(checkpoint_name):\n","        checkpoint_name = os.path.join(\n","            resume_from_checkpoint, 'adapter_model.bin'\n","        )\n","        resume_from_checkpoint = False\n","    if os.path.exists(checkpoint_name):\n","        logger.info(f'Restarting from {checkpoint_name}')\n","        adapters_weights = torch.load(checkpoint_name)\n","        set_peft_model_state_dict(model, adapters_weights)\n","    else:\n","        logger.info(f'Checkpoint {checkpoint_name} not found')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"bJCVEI7F2Qai","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1701553223783,"user_tz":300,"elapsed":140,"user":{"displayName":"Gezheng Bai","userId":"07004837890649195177"}},"outputId":"079cab2f-bd73-4467-c2dc-baab9144b840"},"outputs":[{"output_type":"stream","name":"stdout","text":["trainable params: 2097152 || all params: 6740512768 || trainable%: 0.03111264783824826\n"]}],"source":["print_trainable_parameters(model)"]},{"cell_type":"markdown","metadata":{"id":"XVViHXnayKmy"},"source":["# Load Datasets"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"3VsUelhHyMrn","executionInfo":{"status":"ok","timestamp":1701553230601,"user_tz":300,"elapsed":4890,"user":{"displayName":"Gezheng Bai","userId":"07004837890649195177"}},"colab":{"base_uri":"https://localhost:8080/","height":177,"referenced_widgets":["c4287084334d45d9b004de78d708d3b6","64861a6e500c4f2fb7295dae0e42c785","0387b47c0cdd4db0a83994ad3320e89e","2c498b1b03d94500b6d1ac411b01ec00","cd6eff182f134237b6279e61bf6632f5","757ccb9fc0014157803f23c6e3463077","cd0c8904984343eaac8d351688dedaa3","c9d116a3d22c48d98af5bc8ba622504b","4aca1248189441f7b66f90b838de2208","8111bca76de24c04a826ed1b0209c8f3","1b27521a1d914779966a1eda221032c7","662099bff8d44620b832cba6221087cd","2cb27f30626f489e8b5f1de5d2b7ce0f","3ff41e261eb14d57914aacd7b9e6c350","2e171702dd0742f5bbc427dbb93dc8e2","6fe0992c408340128b32f98e2cc12f24","b11e526c3c3a49e5927ae09a65807bff","bf6d55eee23b4a428ffaeb6c8e191d62","c74e338da23a47f2bd743c7cfca8d3c1","f74b84bd3a3b4e47b38ea23e083ee071","124a687f0ded4050aa6bb08c8f2f37f0","69beccf4535849c8bd673e0422958aca","2cb2d307a25c4fb39860d25ec9e9853d","17e6878cd6ef4d5a882d309ff0f9382d","a68ffdea502647e3ac1255892db2e42d","a84e1855622841b680885c5f56129bab","7905d1b656fd4dfc89fbcd266cb8c1cb","a05ded5fa3c14c4db0c6d8f3b061f87f","950cc3fe8fe543598447be84439f37cb","3e774ce6cae948a9a2b03b28a7e5de93","682c655bb94a46c9a67fa97340d9793c","ef7aaa9bb3c04929b4bbb193b519cf2a","65380abf3b0d45728549ab8cf88b9042","b3eca0272788434dbc5004cf79162f3b","d4b995f5dfb642d4badde0effecc9538","d369c2d9fd304b5ba12c9c0bad51b785","9c2b2adb9e0943738ab4f52f74824a06","3a93e78cf4f64ae58685a16ad25bde1e","8310cd5b1ce54b81969484e3676b6911","1a11a65c16314b5a8c891206f7aaf43a","ff9e4507631d4964ba649e389a305abd","fa1556c556194a408154b531946a3972","d22d2ca180d0480282223197eb88f2e8","f424a063873a42e1932315e3db1c4b44","6366b39386cd402d9569f54296018853","45ad11f9c5a4409ba45042e1e3224840","19dc8fbbaf8f4c539e3e3c6eb0e4d19c","b0282be32862430d9ed6f09b95b84d12","92cd145c3694478dbbff8d6707e74834","a8614c5fd064426381d1d39d17cb54cc","08409f364ecc49fc8e0e4258c18e628f","e528d9018ebd4d37ba952487adadeb2d","df54fb726e9846cc881ac21621b2ec1f","3b82b76b8d9449adb9dd0f48bd02ab4d","0e52e5e85e13464b8de4be0e1c9eb403"]},"outputId":"6434df81-17e4-47d1-943d-3788aae1551b"},"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading readme:   0%|          | 0.00/372 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c4287084334d45d9b004de78d708d3b6"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading data files:   0%|          | 0/1 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"662099bff8d44620b832cba6221087cd"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading data:   0%|          | 0.00/31.3M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"2cb2d307a25c4fb39860d25ec9e9853d"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Extracting data files:   0%|          | 0/1 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b3eca0272788434dbc5004cf79162f3b"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating train split: 0 examples [00:00, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"6366b39386cd402d9569f54296018853"}},"metadata":{}}],"source":["# Load data\n","data = load_dataset('gbharti/wealth-alpaca_lora')\n","data = data.shuffle(seed=RANDOM_SEED)  # Shuffle dataset here"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"WBoxxEf777bD","colab":{"base_uri":"https://localhost:8080/","height":81,"referenced_widgets":["4cf6575e21d0455eaf09a7ce9afa685f","f9bb8fc5c1b54d0997495e27d0fc00f1","1c6df0bbe7884c09b0e9fda347fb1c86","1c3a7562a23c4b86b0b238470cd52a47","690dce4d9dcb41d9a9b2048d97eaf1e4","d96ad39484cb48798366e282950c319c","37fa7ba44cc346f0a8f169e476831894","7cbef49ebff54e2185cefb7f712da977","6a0a4513eac24fe2ab03aad6060f3cfa","7b8942a78805443a8c81326279d51d5f","b1585d61f7094ff8986b81765c95799d","f42cdb8fbff14bcd9f3185e9114c9089","b8bd5486b4e448bbb1b557f6132f86d6","083f0bd04a874bd9999ceec6c0aaaae5","91bd18996df440bc9c4a95eb41000e29","8c3858d4d25e452baa4f42b130b1cd1a","6d3ec5f7442e4fb48139c21753fbca39","535eaa9b038b435cbeac94186cab60fb","f92c757ba1cf4d2592b86222f74e1d18","47ae1ef54c584a21b1aacafa16c8756d","b12baee422b34d04a888ddca5aa0d8ab","8e5470c04ceb43dda3bb1aff7e9545fc"]},"executionInfo":{"status":"ok","timestamp":1701553287600,"user_tz":300,"elapsed":55323,"user":{"displayName":"Gezheng Bai","userId":"07004837890649195177"}},"outputId":"684e3dbd-b1c6-4c12-bb6f-6b90a44fdaa7"},"outputs":[{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/42341 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"4cf6575e21d0455eaf09a7ce9afa685f"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/2000 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"f42cdb8fbff14bcd9f3185e9114c9089"}},"metadata":{}}],"source":["# Train/val split\n","if VAL_SET_SIZE > 0:\n","    train_val = data['train'].train_test_split(\n","        test_size=VAL_SET_SIZE, shuffle=False, seed=RANDOM_SEED\n","    )\n","    train_data = train_val['train'].map(generate_and_tokenize_prompt)\n","    val_data = train_val['test'].map(generate_and_tokenize_prompt)\n","else:\n","    train_data = data['train'].map(generate_and_tokenize_prompt)\n","    val_data = None"]},{"cell_type":"markdown","metadata":{"id":"y9xMUpjx8ETf"},"source":["# Training"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"9h1RMuYF8DFq","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"error","timestamp":1701567558846,"user_tz":300,"elapsed":14256858,"user":{"displayName":"Gezheng Bai","userId":"07004837890649195177"}},"outputId":"31413988-680b-4e09-b82d-717f58671349"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["Tracking run with wandb version 0.16.0"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["Run data is saved locally in <code>/content/wandb/run-20231202_214143-jvspogvz</code>"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["Syncing run <strong><a href='https://wandb.ai/baigzcu123/huggingface/runs/jvspogvz' target=\"_blank\">chocolate-sponge-20</a></strong> to <a href='https://wandb.ai/baigzcu123/huggingface' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":[" View project at <a href='https://wandb.ai/baigzcu123/huggingface' target=\"_blank\">https://wandb.ai/baigzcu123/huggingface</a>"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":[" View run at <a href='https://wandb.ai/baigzcu123/huggingface/runs/jvspogvz' target=\"_blank\">https://wandb.ai/baigzcu123/huggingface/runs/jvspogvz</a>"]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["You're using a LlamaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n","/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/bitsandbytes/autograd/_functions.py:322: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n","  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","    <div>\n","      \n","      <progress value='166' max='165' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [165/165 3:56:24, Epoch 1.00/1]\n","    </div>\n","    <table border=\"1\" class=\"dataframe\">\n","  <thead>\n"," <tr style=\"text-align: left;\">\n","      <th>Step</th>\n","      <th>Training Loss</th>\n","      <th>Validation Loss</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <td>5</td>\n","      <td>4.083300</td>\n","      <td>4.059131</td>\n","    </tr>\n","    <tr>\n","      <td>10</td>\n","      <td>4.064200</td>\n","      <td>4.059364</td>\n","    </tr>\n","    <tr>\n","      <td>15</td>\n","      <td>4.012600</td>\n","      <td>4.059549</td>\n","    </tr>\n","    <tr>\n","      <td>20</td>\n","      <td>4.092000</td>\n","      <td>4.058634</td>\n","    </tr>\n","    <tr>\n","      <td>25</td>\n","      <td>4.035400</td>\n","      <td>4.056879</td>\n","    </tr>\n","    <tr>\n","      <td>30</td>\n","      <td>4.053900</td>\n","      <td>4.055991</td>\n","    </tr>\n","    <tr>\n","      <td>35</td>\n","      <td>4.001800</td>\n","      <td>4.054271</td>\n","    </tr>\n","    <tr>\n","      <td>40</td>\n","      <td>3.892300</td>\n","      <td>4.049274</td>\n","    </tr>\n","    <tr>\n","      <td>45</td>\n","      <td>3.996000</td>\n","      <td>4.043870</td>\n","    </tr>\n","    <tr>\n","      <td>50</td>\n","      <td>3.941700</td>\n","      <td>4.036085</td>\n","    </tr>\n","    <tr>\n","      <td>55</td>\n","      <td>3.972500</td>\n","      <td>4.025451</td>\n","    </tr>\n","    <tr>\n","      <td>60</td>\n","      <td>3.968500</td>\n","      <td>4.009896</td>\n","    </tr>\n","    <tr>\n","      <td>65</td>\n","      <td>3.932600</td>\n","      <td>3.987982</td>\n","    </tr>\n","    <tr>\n","      <td>70</td>\n","      <td>3.998600</td>\n","      <td>3.960882</td>\n","    </tr>\n","    <tr>\n","      <td>75</td>\n","      <td>3.862900</td>\n","      <td>3.927747</td>\n","    </tr>\n","    <tr>\n","      <td>80</td>\n","      <td>3.879600</td>\n","      <td>3.886197</td>\n","    </tr>\n","    <tr>\n","      <td>85</td>\n","      <td>3.824200</td>\n","      <td>3.838368</td>\n","    </tr>\n","    <tr>\n","      <td>90</td>\n","      <td>3.763000</td>\n","      <td>3.784117</td>\n","    </tr>\n","    <tr>\n","      <td>95</td>\n","      <td>3.707500</td>\n","      <td>3.722733</td>\n","    </tr>\n","    <tr>\n","      <td>100</td>\n","      <td>3.649300</td>\n","      <td>3.657145</td>\n","    </tr>\n","    <tr>\n","      <td>105</td>\n","      <td>3.598400</td>\n","      <td>3.588341</td>\n","    </tr>\n","    <tr>\n","      <td>110</td>\n","      <td>3.508500</td>\n","      <td>3.515939</td>\n","    </tr>\n","    <tr>\n","      <td>115</td>\n","      <td>3.450500</td>\n","      <td>3.447148</td>\n","    </tr>\n","    <tr>\n","      <td>120</td>\n","      <td>3.420000</td>\n","      <td>3.386171</td>\n","    </tr>\n","    <tr>\n","      <td>125</td>\n","      <td>3.297400</td>\n","      <td>3.331499</td>\n","    </tr>\n","    <tr>\n","      <td>130</td>\n","      <td>3.255800</td>\n","      <td>3.284029</td>\n","    </tr>\n","    <tr>\n","      <td>135</td>\n","      <td>3.229500</td>\n","      <td>3.243472</td>\n","    </tr>\n","    <tr>\n","      <td>140</td>\n","      <td>3.234600</td>\n","      <td>3.207286</td>\n","    </tr>\n","    <tr>\n","      <td>145</td>\n","      <td>3.179900</td>\n","      <td>3.177900</td>\n","    </tr>\n","    <tr>\n","      <td>150</td>\n","      <td>3.132500</td>\n","      <td>3.154840</td>\n","    </tr>\n","    <tr>\n","      <td>155</td>\n","      <td>3.134700</td>\n","      <td>3.136347</td>\n","    </tr>\n","    <tr>\n","      <td>160</td>\n","      <td>3.096100</td>\n","      <td>3.123148</td>\n","    </tr>\n","  </tbody>\n","</table><p>"]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/bitsandbytes/autograd/_functions.py:322: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n","  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n","/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/bitsandbytes/autograd/_functions.py:322: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n","  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n","/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/bitsandbytes/autograd/_functions.py:322: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n","  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n","/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/bitsandbytes/autograd/_functions.py:322: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n","  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n","/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/bitsandbytes/autograd/_functions.py:322: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n","  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n","/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/bitsandbytes/autograd/_functions.py:322: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n","  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n","/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/bitsandbytes/autograd/_functions.py:322: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n","  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n","/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/bitsandbytes/autograd/_functions.py:322: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n","  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n","/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/bitsandbytes/autograd/_functions.py:322: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n","  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n","/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/bitsandbytes/autograd/_functions.py:322: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n","  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n","/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/bitsandbytes/autograd/_functions.py:322: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n","  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n","/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/bitsandbytes/autograd/_functions.py:322: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n","  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n","/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/bitsandbytes/autograd/_functions.py:322: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n","  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n","/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/bitsandbytes/autograd/_functions.py:322: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n","  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n","/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/bitsandbytes/autograd/_functions.py:322: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n","  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n","/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/bitsandbytes/autograd/_functions.py:322: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n","  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n"]},{"output_type":"error","ename":"FailedPreconditionError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mFailedPreconditionError\u001b[0m                   Traceback (most recent call last)","\u001b[0;32m<ipython-input-19-f373a6f15e6c>\u001b[0m in \u001b[0;36m<cell line: 44>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     42\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m \u001b[0;31m# Train\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 44\u001b[0;31m 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\u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1232\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mwriter\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall_writers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1233\u001b[0;31m             \u001b[0mwriter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflush\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1234\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1235\u001b[0m     \u001b[0;32mdef\u001b[0m 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\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_writer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflush\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    191\u001b[0m             \u001b[0;31m# Check the status again in case the background worker thread has\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    192\u001b[0m             \u001b[0;31m# failed in the meantime to avoid waiting until the next call to\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorboard/summary/writer/record_writer.py\u001b[0m in \u001b[0;36mflush\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     41\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     42\u001b[0m     \u001b[0;32mdef\u001b[0m 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model\n","\n","trainer = transformers.Trainer(\n","    model=model,\n","    train_dataset=train_data,\n","    eval_dataset=val_data,\n","    # trainer argument config\n","    args=transformers.TrainingArguments(\n","        seed=RANDOM_SEED,  # Reproducibility\n","        data_seed=RANDOM_SEED,\n","        per_device_train_batch_size=MICRO_BATCH_SIZE,\n","        gradient_accumulation_steps=GRADIENT_ACCUMULATION_STEPS,\n","        warmup_steps=100,\n","        num_train_epochs=EPOCHS,\n","        learning_rate=LEARNING_RATE,\n","        fp16=True,\n","        logging_steps=5,\n","        evaluation_strategy='steps' if VAL_SET_SIZE > 0 else 'no',\n","        save_strategy='steps',\n","        save_steps=10,\n","        eval_steps=5 if VAL_SET_SIZE > 0 else None,\n","        output_dir=OUTPUT_DIR,\n","        save_total_limit=3,\n","        load_best_model_at_end=True if VAL_SET_SIZE > 0 else False,\n","        ddp_find_unused_parameters=False if ddp else None,\n","    ),\n","\n","    data_collator=transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False),\n",")\n","model.config.use_cache = False\n","\n","# PEFT setup\n","old_state_dict = model.state_dict\n","model.state_dict = (\n","    lambda self, *_, **__: get_peft_model_state_dict(self, old_state_dict())\n",").__get__(model, type(model))\n","\n","# Use the latest PyTorch 2.0 if possible\n","if torch.__version__ >= '2' and sys.platform != 'win32':\n","    model = torch.compile(model)\n","\n","# Train\n","trainer.train()\n","wandb.finish()"]},{"cell_type":"code","source":["### Merge the LoRA adaptor to Base model\n","\n","# Step 1: Extract LoRA Weights from the Trained Model\n","lora_weights = get_peft_model_state_dict(model)\n","\n","# Step 2: Load the Original Llama2-7b Model\n","original_model = AutoModelForCausalLM.from_pretrained(model_name)\n","\n","# Step 3: Update the Original Model with LoRA Weights\n","for key, value in lora_weights.items():\n","    if key in original_model.state_dict():\n","        original_model.state_dict()[key].data.copy_(value.data)\n","\n","# Step 4: Save the Updated Model\n","# Specify your desired path to save the model\n","updated_model_path = os.path.join(OUTPUT_DIR, \"Llama2-7b_Finance_FT_3_with_lora\")\n","original_model.save_pretrained(updated_model_path)\n","\n","print(\"LoRA parameters have been successfully merged into the original Llama2-7b model.\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":248,"referenced_widgets":["e6514b1a96564e0d92cd46201a28aff4","dc4189df8af342d280558171369f8fb4","6f496ab5cb2f403c82b811e2fca59b55","55bc3ed5d31d49bf8ccf78260b1aee09","b2cb20c5c6524a5182a45b23620932e1","87866ce2b1c147b0832c1c005b966f1e","1073b211d58941dea3cd5d22b6059da8","a3f66a9b8c4549eea33f011801f63267","4600a9532b7d4d50a4dc49e39a856c7d","f05a3c515b844d71a4fda552e9bea3b6","de3d4d723ea74cd88ba6dcc8d75311e0"]},"id":"UJXt7XfxR-Y5","executionInfo":{"status":"ok","timestamp":1701567864401,"user_tz":300,"elapsed":147678,"user":{"displayName":"Gezheng Bai","userId":"07004837890649195177"}},"outputId":"7b85aae6-3546-4407-e84b-1e4598d71ba1"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e6514b1a96564e0d92cd46201a28aff4"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/transformers/generation/configuration_utils.py:381: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.9` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`. This was detected when initializing the generation config instance, which means the corresponding file may hold incorrect parameterization and should be fixed.\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/generation/configuration_utils.py:386: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.6` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`. This was detected when initializing the generation config instance, which means the corresponding file may hold incorrect parameterization and should be fixed.\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/generation/configuration_utils.py:527: UserWarning: The generation config instance is invalid -- `.validate()` throws warnings and/or exceptions. Fix these issues to save the configuration. This warning will be raised to an exception in v4.34.\n","\n","Thrown during validation:\n","`do_sample` is set to `False`. However, `temperature` is set to `0.9` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["LoRA parameters have been successfully merged into the original Llama2-7b model.\n"]}]},{"cell_type":"code","source":["from transformers import AutoModel, AutoTokenizer, AutoModelForCausalLM, LlamaForCausalLM, LlamaTokenizerFast   # 4.30.2\n","from peft import PeftModel  # 0.5.0\n","base_model = \"NousResearch/Llama-2-13b-hf\"\n","peft_model = \"finetuned_model\"\n","tokenizer = LlamaTokenizerFast.from_pretrained(base_model, trust_remote_code=True)\n","tokenizer.pad_token = tokenizer.eos_token\n","model = LlamaForCausalLM.from_pretrained(base_model, trust_remote_code=True, device_map = \"cuda:0\", load_in_8bit = True,)\n","model = PeftModel.from_pretrained(model, peft_model)"],"metadata":{"id":"L1XchxEXQhof"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"zjJBJtorlzoh"},"source":["# push to hub"]},{"cell_type":"code","source":["!git init \"/content/drive/MyDrive/Hallucination/Llama2-7b_Finance_FT_3/git\"\n","!git remote add origin YOUR_REPO_URL\n"],"metadata":{"id":"Jr2yQLAkqbhf","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1701568023610,"user_tz":300,"elapsed":808,"user":{"displayName":"Gezheng Bai","userId":"07004837890649195177"}},"outputId":"cec2c063-47f7-4548-9537-5eb1db787e09"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[33mhint: Using 'master' as the name for the initial branch. This default branch name\u001b[m\n","\u001b[33mhint: is subject to change. To configure the initial branch name to use in all\u001b[m\n","\u001b[33mhint: of your new repositories, which will suppress this warning, call:\u001b[m\n","\u001b[33mhint: \u001b[m\n","\u001b[33mhint: \tgit config --global init.defaultBranch <name>\u001b[m\n","\u001b[33mhint: \u001b[m\n","\u001b[33mhint: Names commonly chosen instead of 'master' are 'main', 'trunk' and\u001b[m\n","\u001b[33mhint: 'development'. The just-created branch can be renamed via this command:\u001b[m\n","\u001b[33mhint: \u001b[m\n","\u001b[33mhint: \tgit branch -m <name>\u001b[m\n","Initialized empty Git repository in /content/drive/MyDrive/Hallucination/Llama2-7b_Finance_FT_3/git/.git/\n","fatal: not a git repository (or any of the parent directories): .git\n"]}]},{"cell_type":"code","execution_count":null,"metadata":{"id":"CEpEFeD-h0Ye"},"outputs":[],"source":["from huggingface_hub import notebook_login\n","\n","notebook_login()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ybLo27HHmfnD"},"outputs":[],"source":["from huggingface_hub import HfApi, Repository\n","\n","model_name = \"Llama2-7b_Finance_lora_1\"\n","model_path = OUTPUT_DIR\n","repo_path = \"/content/drive/MyDrive/Hallucination/Llama2-7b_Finance_FT_1/git\"\n","repo_name = \"Llama2-7b_Finance_lora_1\"  # Replace with your desired repository name\n","\n","# Create a repository on the Hugging Face Hub\n","api = HfApi()\n","# Pass the 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